• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.137-153
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• 2016

The price of agricultural products has changed from year to year, the m ajor c ause o f price fluctuation is the imbalance of supply and demand. Materials which are mainly used in korean cabbage production volume is the forecast model, using the cadastral result, slope calculation is impossible to achieved. For this reason, this implies the drastic decrease of prices and the prediction of supply and demand of field crops that is cultivated in a highland slope area, this situation is being repeated. Therefore, the target area of this research is the slopes of high land, by using 2D and 3D Lidar data for the analysis of the cultivated area. Experiment was carried out in the same area to compare the data differences. The rate of change in the area of slope is quantitatively increasing presented by the regression model. An alternative methodology that can improve the reliability of the calculated slope area using 2D is through cadastral map.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.195-220
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• 2018

In this study, we conducted an empirical analysis of the factors that affect the change of Bitcoin Closing Price. Previous studies have focused on the security of the block chain system, the economic ripple effects caused by the cryptocurrency, legal implications and the acceptance to consumer about cryptocurrency. In various area, cryptocurrency was studied and many researcher and people including government, regardless of country, try to utilize cryptocurrency and applicate to its technology. Despite of rapid and dramatic change of cryptocurrencies' price and growth of its effects, empirical study of the factors affecting the price change of cryptocurrency was lack. There were only a few limited studies, business reports and short working paper. Therefore, it is necessary to determine what factors effect on the change of closing Bitcoin price. For analysis, hypotheses were constructed from three dimensions of consumer, industry, and macroeconomics for analysis, and time series data were collected for variables of each dimension. Consumer variables consist of search traffic of Bitcoin, search traffic of bitcoin ban, search traffic of ransomware and search traffic of war. Industry variables were composed GPU vendors' stock price and memory vendors' stock price. Macro-economy variables were contemplated such as U.S. dollar index futures, FOMC policy interest rates, WTI crude oil price. Using above variables, we did times series regression analysis to find relationship between those variables and change of Bitcoin Closing Price. Before the regression analysis to confirm the relationship between change of Bitcoin Closing Price and the other variables, we performed the Unit-root test to verifying the stationary of time series data to avoid spurious regression. Then, using a stationary data, we did the regression analysis. As a result of the analysis, we found that the change of Bitcoin Closing Price has negative effects with search traffic of 'Bitcoin Ban' and US dollar index futures, while change of GPU vendors' stock price and change of WTI crude oil price showed positive effects. In case of 'Bitcoin Ban', it is directly determining the maintenance or abolition of Bitcoin trade, that's why consumer reacted sensitively and effected on change of Bitcoin Closing Price. GPU is raw material of Bitcoin mining. Generally, increasing of companies' stock price means the growth of the sales of those companies' products and services. GPU's demands increases are indirectly reflected to the GPU vendors' stock price. Making an interpretation, a rise in prices of GPU has put a crimp on the mining of Bitcoin. Consequently, GPU vendors' stock price effects on change of Bitcoin Closing Price. And we confirmed U.S. dollar index futures moved in the opposite direction with change of Bitcoin Closing Price. It moved like Gold. Gold was considered as a safe asset to consumers and it means consumer think that Bitcoin is a safe asset. On the other hand, WTI oil price went Bitcoin Closing Price's way. It implies that Bitcoin are regarded to investment asset like raw materials market's product. The variables that were not significant in the analysis were search traffic of bitcoin, search traffic of ransomware, search traffic of war, memory vendor's stock price, FOMC policy interest rates. In search traffic of bitcoin, we judged that interest in Bitcoin did not lead to purchase of Bitcoin. It means search traffic of Bitcoin didn't reflect all of Bitcoin's demand. So, it implies there are some factors that regulate and mediate the Bitcoin purchase. In search traffic of ransomware, it is hard to say concern of ransomware determined the whole Bitcoin demand. Because only a few people damaged by ransomware and the percentage of hackers requiring Bitcoins was low. Also, its information security problem is events not continuous issues. Search traffic of war was not significant. Like stock market, generally it has negative in relation to war, but exceptional case like Gulf war, it moves stakeholders' profits and environment. We think that this is the same case. In memory vendor stock price, this is because memory vendors' flagship products were not VRAM which is essential for Bitcoin supply. In FOMC policy interest rates, when the interest rate is low, the surplus capital is invested in securities such as stocks. But Bitcoin' price fluctuation was large so it is not recognized as an attractive commodity to the consumers. In addition, unlike the stock market, Bitcoin doesn't have any safety policy such as Circuit breakers and Sidecar. Through this study, we verified what factors effect on change of Bitcoin Closing Price, and interpreted why such change happened. In addition, establishing the characteristics of Bitcoin as a safe asset and investment asset, we provide a guide how consumer, financial institution and government organization approach to the cryptocurrency. Moreover, corroborating the factors affecting change of Bitcoin Closing Price, researcher will get some clue and qualification which factors have to be considered in hereafter cryptocurrency study.

• Smart Media Journal
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• v.7 no.1
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• pp.45-51
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• 2018

Avian influenza (AI) is notorious for its rapid infection rate, and has a serious impact on consumers and producers alike, especially in poultry farms. The AI outbreak, which occurred nationwide at the end of 2016, devastated the livestock farming industries. As a result, the prices of eggs and egg products had skyrocketed, and the event was reported by the media with heavy emphasis. The purpose of this study was to investigate the correlation between the egg price fluctuation and the keyword changes in online news articles reflecting social issues. To this end, we analyzed 682 cases of AI-related online news articles for fourteen weeks from November 2016 in South Korea. The results of this study are expected to contribute to understanding the relationship between the actual price of eggs and the keywords from news articles related to social issues.

• The Journal of Society for e-Business Studies
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• v.20 no.2
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• pp.1-14
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• 2015

In this paper, we investigate the relationship between Internet search trends and stock market. Under the assumption that investors may use Internet search engine to obtain information for companies of their interests before taking actual investment actions, the relationship between the changes on Internet search volume and the fluctuation of trading volume as well as stock price of a company is analyzed with actual market data. A search trend investment strategy that reflects the changes on Internet search volume is applied to large enterprises' group and to small and medium enterprises' (SMEs) group, and the correlation between profit rate and trading volume is analyzed for each company group. Our search trend investment strategy has outperformed average stock market returns in both KOSPI and KOSDAQ markets during the seven-year study period (2007~2013). It is also shown that search trend investment strategy is more effective to SMEs than to large enterprises. The relationship between changes on Internet search volume and stock trading volume is stronger at SMEs than at large enterprises.

• Food Science and Preservation
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• v.3 no.1
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• pp.61-75
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• 1996

Apple is most favorable fruit in Korea, and apple farmland has been increased before and after the agreements of Uruguay Round and apple is considered as one of strategic agricultural products. Especially expansion of apple farmland is concentrated in Kyungpook region because of the suitable climate and its market share is about 70 percents in 1992. The marketing channels of apples of Kyungpook region are widely classified by merchant, agricultural or horticultural co-op and large scale farmer's. Among them market share of merchant's occupy over 65 percent. In marketing margins, commercial profit is higher than cost in total marketing margins and, assembler and retailer's margin is not less than wholesaler's. The fluctuation of the price of apple is high in year. The marketing problems of apples are several, first, complex marketing channels, secondly, the high percentage of market share by growing district assembler, thirdly, low rate of package and quality standardization, finally, concentration of shipment of apple because of the shortage of apple processing, storage and marketing facilities, of newly produced apples. In conclusion, to increase apple grower's income with the stabilization of supply and quality upgrade, the improvement measures of marketing system are as follows, first, government level's support in marketing facilities and mechanism, secondly, the increment of supply by grower's cooperatives, thirdly, the establishment of a serious of marketing system to increase the efficiency, fourthly, the establishment of cold-chain system and quality standardization of apple, finally, production of various kinds of apple processing goods.

• The KSFM Journal of Fluid Machinery
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• v.15 no.6
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• pp.25-30
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• 2012

Renewable energy has been interested because of fluctuation of oil price, depletion of fossil fuel resources and environmental impact. Amongst renewable energy resources, hydropower is most reliable and cost effective way. In this study, to develop a new type of micro hydro turbine which can be operated in the range of very low specific speed, a cross-flow hydro turbine with simple structure is proposed. The turbine is designed to be used at the very low specific speed range of hydropower resources, such as very high-head and considerably small-flow rate water resources. CFD analysis on the performance and internal flow characteristics of the turbine is conducted to obtain a practical data for the new design method of the turbine. Results show that optimized arrangement of guide vane angle and inner guide angle can give contribution to the turbine performance improvement.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.371-375
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• 2008

Nowadays, Korean Housing Market have been unstable because of the global economic fluctuation such as steady decline in the interest rate and the house price bubble. While Korean Government policy responses these state, rapidly changing policies led to deep confusion in the Korean Housing Market. In this situation, Analysis for housing market forecasting has been partial and fragmentary, therefore comprehensive solution and systematical approach is required to analyze the housing market including causal nexus between market determining factors. In an integrated point of view, applying the system dynamics modeling, the paper aims at proposing basic Korean housing market dynamics models based on Fundamental principles of housing market determined by supply and demand.

• Korean Journal of Culture and Social Issue
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• v.29 no.3
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• pp.361-404
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• 2023

This study utilized a cross-temporal meta-analysis to explore shifts in self-efficacy levels among Korean college students from 1999 to 2022. We expected that increases in authoritative parenting styles, narcissism levels among students, and individualism in Korea might have positively influenced the self-efficacy of college students over the years. Conversely, growing economic disparities, decreasing class mobility, and the increasing instability of job markets might have had negative effects on self-efficacy. To investigate this, we analyzed 293 self-efficacy studies involving Korean college students published between 1999 and 2022, encompassing a total of 88,904 participants. Our criteria included studies that used the three most prevalent self-efficacy scales in Korea, focused solely on Korean college students, were cross-sectional with a one-time self-efficacy measurement, and provided essential statistics for our analysis. The results indicated no significant change in the self-efficacy levels of Korean college students over the observed period from 1999 to 2022. Additionally, we examined correlations between self-efficacy and various social indicators from different time points (20, 15, 10, and 5 years prior, as well as the year of data collection). Findings revealed that both birth rate and consumer price fluctuation rate were consistently negatively correlated with self-efficacy, while gross national income was positively correlated. This study is the first to assess Korean college students' self-efficacy levels using a cross-temporal meta-analysis, offering foundational knowledge for implementing such analytical methods for subsequent research and providing an indirect assessment of the generational gap theory. Finally, the limitations of the study and the direction for future research were discussed.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.480-488
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• 2016

As the subprime mortgage crisis spread globally, it depressed not only the financial market, but also the construction business in Korea. In fact, according to CERIK, the BSI of the construction businesses plunged from 80 points in December 2006 to 14.6 points in November 2008, and the extent of the depression in the housing sector was particularly serious. In this respect, this paper analyzes the influence of the financial market fluctuation on the housing market before and after the Global Financial Crisis using VECM. The periods from January 2000 to December 2007 and January 2008 to October 2015, before and after the financial crisis, were set as Models 1 and 2, respectively. The results are as follows. First, when the economy is good, the Gangnam housing market is an attractive one for investment. However, when it is depressed, the Gangnam housing market changes in response to the macroeconomic fluctuations. Second, the Gangbuk and Gangnam housing markets showed different responses to fluctuations in the financial market. Third, when the economy is bad, the effect of low interest rates is limited, due to the housing market risk.